Velocity-Based Resistance Training Monitoring and Prescription: The Good, the Bad, and the Alternatives
Traditionally, resistance training (RT) is prescribed using percent-based training (PBT) methods that use the loads relative to a maximal load lifted for one repetition (%1RM) and set volume based on the theoretical relationship between a maximum number of repetitions individuals can do with a given %1RM. However, PBT cannot account for performance fluctuations. Thus, velocity-based approaches emerged from research showing that 1) load-velocity profiles (LVPs) can be used to adjust load per readiness; and 2) velocity loss (VL) can be used to terminate sets per the desired level of neuromuscular fatigue. While the reliability and utility of LVPs and confounding factors affecting them were previously examined, minimal research existed on the variability of acute responses to VL thresholds. Even assuming no variability, VL thresholds require costly velocity monitoring devices. Therefore, the purpose of this thesis was to examine novel velocity-based approaches while considering the effects of sex, training history, relative strength, and personality on their accuracy, and reduced or no-cost means of implementing VL thresholds. In Chapter 1, the benefits and shortcomings of velocity-based approaches, the thesis aims, and the structure are described. Chapter 2 consists of a systematic review and meta-analysis of acute and chronic effects of VL thresholds, highlighting VL threshold selection as an important consideration and high inter-individual variability in responses to different VL thresholds. In Chapter 3, the reproducibility and sensitivity of velocity monitoring devices of varying prices are examined, with GymAware emerging as the most reproducible and sensitive device. A cheaper alternative is the Vmaxpro; however, only if mean velocity is used. The variability in work completed before reaching different VL thresholds and factors impacting this variability are investigated in Chapter 4. Notably, work completed varies across people and testing sessions and is influenced by load, sex, training status, history, and personality. In Chapter 5, the validity of the relationship between VL and the percentage of repetitions completed out of the maximum possible (%repetitions) is examined. While relationships are highly individual, both general and individual VL- %repetitions relationships poorly predict %repetitions in subsequent testing sessions (prediction error > 10%). The validity of the fastest repetition in a set to predict the maximum number of repetitions that can be completed (XRM) is investigated in Chapter 6. Importantly, individual, but not general XRM-velocity relationships are valid (< 1.91 repetition errors) and are unaffected by sex, training status, history, or personality. In Chapter 7, the utility of modelling repetitions in reserve-velocity (RIR-velocity) relationships is examined. Again, individual, but not general RIR-velocity relationships have excellent goodness of fit (R2 = 0.91-0.97; residual standard error [RSE] = 0.35-1.38); and acceptable prediction accuracy (< 2 repetitions) and can be averaged across loads without sacrificing accuracy. In Chapters 8 and 9, the effectiveness of cluster set (CS) and rest redistribution (RR) set structures for alleviating fatigue and inducing adaptations are examined, respectively. Both CS and RR effectively reduce mechanical fatigue, perceptual exertion, and metabolic stress and both induce similar or superior adaptations and performance improvements versus traditional sets (TS). In Chapter 10, whether CS and RR can be used as no-cost alternatives to RT monitoring and prescription method using VL is examined. Both CS and RR reduce acute fatigue and maintain repetition velocity above 20% VL. Finally, in Chapter 11, the thesis findings and practical applications are summarised and avenues for future research are explored.